Upload 5 files

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f720384e204e2cb1dec5f8b14c387d771609eaad8d6ddfb6b4992284df045547
+size 133

best2.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c71ac9f83a1690701d31927208ed8c5fee225652564b790547a046823633d65
+size 133

detect_app.py

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+from ultralytics import YOLO 
+import streamlit as st #hh
+import cv2
+import numpy as np
+from skimage.io import imsave
+st.title("Hi,This is only test web application for extracting the Wall from image")
+st.write("Made by otman heddouch")
+st.write("Please upload image of house with Roof ,then the Wamm extracted from the image will be shown above after seconds.")
+st.info("make sure the wall is very clear on the image")
+#load the model
+@st.cache_resource
+def load_model_():
+     model = YOLO("best2.pt")
+     return model
+
+
+model = load_model_()
+
+image_streamlit  = st.file_uploader("Upload your image",type=["jpg","png","jpeg"])
+
+if image_streamlit != None:
+     st.image(image_streamlit)
+     with open("image.jpg",mode = "wb") as f:
+         f.write(image_streamlit.getbuffer())
+     img = model.predict(source="image.jpg",
+                         stream=True, retina_masks=True)
+     new = None
+     original_img = cv2.imread("image.jpg")
+     for result in img:
+         mask = result.masks.cpu().numpy()
+         #cv2.imwrite("mask.jpg",mask.masks)
+         masks = mask.masks.astype(bool)
+         print(masks)
+         ori_img = result.orig_img
+         new = np.ones_like(ori_img, dtype=np.uint8)
+         new_ = np.ones_like(ori_img, dtype=np.uint8)
+
+         for m in masks:
+             new[m] = ori_img[m]
+             new_[m] = 0
+         cv2.imwrite("modified_image.png", new)
+         #cv2.imwrite("modified_image_.png", new_)
+     
+         st.image("modified_image.png")
+          
+      
+
+# from ultralytics import YOLO
+# import streamlit as st
+# import cv2
+# import numpy as np
+
+# st.title("Wall Extraction and Color Change")
+# st.write("Made by Otman Heddouch")
+# st.write("Please upload an image of a house with a roof. The extracted wall will be displayed above after a few seconds.")
+# st.info("Make sure the wall is clear in the image.")
+
+# # Load the model
+# @st.cache_resource
+# def load_model():
+#     model = YOLO("wall.pt")
+#     return model
+
+# model = load_model()
+
+# image_streamlit = st.file_uploader("Upload your image", type=["jpg", "png", "jpeg"])
+
+# if image_streamlit is not None:
+#     st.image(image_streamlit)
+#     with open("image.jpg", mode="wb") as f:
+#         f.write(image_streamlit.getbuffer())
+    
+#     img = model.predict(source="image.jpg", stream=True, retina_masks=True)
+#     original_img = cv2.imread("image.jpg")
+    
+#     for result in img:
+#         mask = result.masks.cpu().numpy()
+#         masks = mask.masks.astype(bool)
+        
+#         modified_img = original_img.copy()
+#         new_color = np.array([0, 255, 0], dtype=np.uint8)  # Green color, you can adjust the values accordingly
+#         modified_img[masks] = new_color
+        
+#         cv2.imwrite("modified_image.png", modified_img)
+#         st.image("modified_image.png")
+

package.txt

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+libgl1

requirements.txt

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+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python-headless
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1  # Google Colab version
+torch>=1.7.0
+torchvision>=0.8.1
+scikit-image
+tqdm>=4.41.0
+protobuf<4.21.5  # https://github.com/ultralytics/yolov5/issues/8012
+
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop  # FLOPs computation
+streamlit
+wget
+ultralytics